04-05-2025, 05:57 AM
LangChain Crash Course
![[Image: th_evOWRe4io14BqGhA9a1oc1CObyFgkf37.avif]](https://sanet.pics/storage-11/0425/avif/th_evOWRe4io14BqGhA9a1oc1CObyFgkf37.avif)
Published 4/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 37m | Size: 222 MB
Learn LangChain, its components, and how it can be used with RAG to set up a QA chain for summarizing documents.
What you'll learn
Learn LangChain from scratch
Understand the LangChain workflow
Summarize multiple PDF documents with LangChain and RAG
Understand chaining in LangChain
Get to know the LangChain components with examples
Load and parse the PDF documents
Split documents into chunks
Setup the embedding models
Learn to create a vector store from the document chunks
Setup a local LLM
Learn to create a QA chain
Requirements
A computer with an Internet
You should be able to use a web browser at a beginner level
Description
Welcome to the LangChain course. LangChain is a framework designed to build applications powered by large language models (LLMs). It provides tools and abstractions to make it easier to integrate LLMs into applications, enabling tasks like question answering, text generation, retrieval-augmented generation (RAG), chatbots, and more.LangChain - Use CasesHere are some of the use cases of LangChain:Question Answering: Build systems that answer questions by retrieving relevant information and generating answers using LLMs.Chatbots: Create conversational agents that can maintain context across interactions.Retrieval-Augmented Generation (RAG): Combine retrieval of relevant documents with text generation for more accurate and context-aware responses.Text Summarization: Generate summaries of long documents or articles.Code Generation: Build tools that generate code based on natural language descriptions.Personal Assistants: Create virtual assistants that can perform tasks like scheduling, email drafting, or information retrieval.Course LessonsLangChain - Introduction1. LangChain - Introduction, Features, and Use Cases2. What is Chaining in LangChainLangChain - Components3. Components/ Modules of LangChain4. Preprocessing Component of LangChain5. Models Component of LangChain6. Prompts Component of LangChain7. Memory Component of LangChain8. Chains Component of LangChain9. Indexes Component of LangChain10. Agents Component of LangChainLangChain with RAG11. LangChain with RAG - Workflow12. LangChain with RAG - Process13. LangChain with RAG - Final Coding Example
Who this course is for
Those who want to begin their AI journey
Beginner AI Enthusiasts
Learn LangChain with RAG
Those who want to understand chaining in LangChain
Those who want to summarize multiple PDF documents
Homepage:
Screenshots
![[Image: th_yLjMEaMYmPpctrJ9SFC8VvoVga7AeTL4.avif]](https://sanet.pics/storage-11/0425/avif/th_yLjMEaMYmPpctrJ9SFC8VvoVga7AeTL4.avif)
Download link
rapidgator.net:
nitroflare.com:
Published 4/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 37m | Size: 222 MB
Learn LangChain, its components, and how it can be used with RAG to set up a QA chain for summarizing documents.
What you'll learn
Learn LangChain from scratch
Understand the LangChain workflow
Summarize multiple PDF documents with LangChain and RAG
Understand chaining in LangChain
Get to know the LangChain components with examples
Load and parse the PDF documents
Split documents into chunks
Setup the embedding models
Learn to create a vector store from the document chunks
Setup a local LLM
Learn to create a QA chain
Requirements
A computer with an Internet
You should be able to use a web browser at a beginner level
Description
Welcome to the LangChain course. LangChain is a framework designed to build applications powered by large language models (LLMs). It provides tools and abstractions to make it easier to integrate LLMs into applications, enabling tasks like question answering, text generation, retrieval-augmented generation (RAG), chatbots, and more.LangChain - Use CasesHere are some of the use cases of LangChain:Question Answering: Build systems that answer questions by retrieving relevant information and generating answers using LLMs.Chatbots: Create conversational agents that can maintain context across interactions.Retrieval-Augmented Generation (RAG): Combine retrieval of relevant documents with text generation for more accurate and context-aware responses.Text Summarization: Generate summaries of long documents or articles.Code Generation: Build tools that generate code based on natural language descriptions.Personal Assistants: Create virtual assistants that can perform tasks like scheduling, email drafting, or information retrieval.Course LessonsLangChain - Introduction1. LangChain - Introduction, Features, and Use Cases2. What is Chaining in LangChainLangChain - Components3. Components/ Modules of LangChain4. Preprocessing Component of LangChain5. Models Component of LangChain6. Prompts Component of LangChain7. Memory Component of LangChain8. Chains Component of LangChain9. Indexes Component of LangChain10. Agents Component of LangChainLangChain with RAG11. LangChain with RAG - Workflow12. LangChain with RAG - Process13. LangChain with RAG - Final Coding Example
Who this course is for
Those who want to begin their AI journey
Beginner AI Enthusiasts
Learn LangChain with RAG
Those who want to understand chaining in LangChain
Those who want to summarize multiple PDF documents
Homepage:
Screenshots
Download link
rapidgator.net:
nitroflare.com:
